Peptide Amphiphiles as Biodegradable Adjuvants for Efficient Retroviral Gene Delivery.

Retroviral gene delivery is the key technique for in vitro and ex vivo gene therapy. However, inefficient virion-cell attachment resulting in low gene transduction efficacy remains a major challenge in clinical applications. Adjuvants for ex vivo therapy settings need to increase transduction efficiency while being easily removed or degraded post-transduction to prevent the risk of venous embolism after infusing the transduced cells back to the bloodstream of patients, yet no such peptide system have been reported thus far. In this study, peptide amphiphiles (PAs) with a hydrophobic fatty acid and a hydrophilic peptide moiety that reveal enhanced viral transduction efficiency are introduced. The PAs form ß-sheet-rich fibrils that assemble into positively charged aggregates, promoting virus adhesion to the cell membrane. The block-type amphiphilic sequence arrangement in the PAs ensures efficient cell-virus interaction and biodegradability. Good biodegradability is observed for fibrils forming small aggregates and it is shown that via molecular dynamics simulations, the fibril-fibril interactions of PAs are governed by fibril surface hydrophobicity. These findings establish PAs as additives in retroviral gene transfer, rivalling commercially available transduction enhancers in efficiency and degradability with promising translational options in clinical gene therapy applications.

Optimized peptide nanofibrils as efficient transduction enhancers for in vitro and ex vivo gene transfer.

Chimeric antigen receptor (CAR)-T cell therapy is a groundbreaking immunotherapy for cancer. However, the intricate and costly manufacturing process remains a hurdle. Improving the transduction rate is a potential avenue to cut down costs and boost therapeutic efficiency. Peptide nanofibrils (PNFs) serve as one such class of transduction enhancers. PNFs bind to negatively charged virions, facilitating their active engagement by cellular protrusions, which enhances virion attachment to cells, leading to increased cellular entry and gene transfer rates. While first-generation PNFs had issues with aggregate formation and potential immunogenicity, our study utilized in silico screening to identify short, endogenous, and non-immunogenic peptides capable of enhancing transduction. This led to the discovery of an 8-mer peptide, RM-8, which forms PNFs that effectively boost T cell transduction rates by various retroviral vectors. A subsequent structure-activity relationship (SAR) analysis refined RM-8, resulting in the D4 derivative. D4 peptide is stable and assembles into smaller PNFs, avoiding large aggregate formation, and demonstrates superior transduction rates in primary T and NK cells. In essence, D4 PNFs present an economical and straightforward nanotechnological tool, ideal for refining ex vivo gene transfer in CAR-T cell production and potentially other advanced therapeutic applications.

Data-mining unveils structure-property-activity correlation of viral infectivity enhancing self-assembling peptides.

Gene therapy via retroviral vectors holds great promise for treating a variety of serious diseases. It requires the use of additives to boost infectivity. Amyloid-like peptide nanofibers (PNFs) were shown to efficiently enhance retroviral gene transfer. However, the underlying mode of action of these peptides remains largely unknown. Data-mining is an efficient method to systematically study structure-function relationship and unveil patterns in a database. This data-mining study elucidates the multi-scale structure-property-activity relationship of transduction enhancing peptides for retroviral gene transfer. In contrast to previous reports, we find that not the amyloid fibrils themselves, but rather µm-sized ß-sheet rich aggregates enhance infectivity. Specifically, microscopic aggregation of ß-sheet rich amyloid structures with a hydrophobic surface pattern and positive surface charge are identified as key material properties. We validate the reliability of the amphiphilic sequence pattern and the general applicability of the key properties by rationally creating new active sequences and identifying short amyloidal peptides from various pathogenic and functional origin. Data-mining-even for small datasets-enables the development of new efficient retroviral transduction enhancers and provides important insights into the diverse bioactivity of the functional material class of amyloids.

Inverse design of viral infectivity-enhancing peptide fibrils from continuous protein-vector embeddings.

Amyloid-like nanofibers from self-assembling peptides can promote viral gene transfer for therapeutic applications. Traditionally, new sequences are discovered either from screening large libraries or by creating derivatives of known active peptides. However, the discovery of de novo peptides, which are sequence-wise not related to any known active peptides, is limited by the difficulty to rationally predict structure-activity relationships because their activities typically have multi-scale and multi-parameter dependencies. Here, we used a small library of 163 peptides as a training set to predict de novo sequences for viral infectivity enhancement using a machine learning (ML) approach based on natural language processing. Specifically, we trained an ML model using continuous vector representations of the peptides, which were previously shown to retain relevant information embedded in the sequences. We used the trained ML model to sample the sequence space of peptides with 6 amino acids to identify promising candidates. These 6-mers were then further screened for charge and aggregation propensity. The resulting 16 new 6-mers were tested and found to be active with a 25% hit rate. Strikingly, these de novo sequences are the shortest active peptides for infectivity enhancement reported so far and show no sequence relation to the training set. Moreover, by screening the sequence space, we discovered the first hydrophobic peptide fibrils with a moderately negative surface charge that can enhance infectivity. Hence, this ML strategy is a time- and cost-efficient way for expanding the sequence space of short functional self-assembling peptides exemplified for therapeutic viral gene delivery.

The C-terminal 32-mer fragment of hemoglobin alpha is an amyloidogenic peptide with antimicrobial properties.

Antimicrobial peptides (AMPs) are major components of the innate immune defense. Accumulating evidence suggests that the antibacterial activity of many AMPs is dependent on the formation of amyloid-like fibrils. To identify novel fibril forming AMPs, we generated a spleen-derived peptide library and screened it for the presence of amyloidogenic peptides. This approach led to the identification of a C-terminal 32-mer fragment of alpha-hemoglobin, termed HBA(111-142). The non-fibrillar peptide has membranolytic activity against various bacterial species, while the HBA(111-142) fibrils aggregated bacteria to promote their phagocytotic clearance. Further, HBA(111-142) fibrils selectively inhibited measles and herpes viruses (HSV-1, HSV-2, HCMV), but not SARS-CoV-2, ZIKV and IAV. HBA(111-142) is released from its precursor by ubiquitous aspartic proteases under acidic conditions characteristic at sites of infection and inflammation. Thus, HBA(111-142) is an amyloidogenic AMP that may specifically be generated from a highly abundant precursor during bacterial or viral infection and may play an important role in innate antimicrobial immune responses.